Purpose – The purpose of this paper is to test whether the qualitative variables regarding the territory and the firm–territory relationship can improve the accuracy rates of small business default prediction models. Design/methodology/approach – The authors apply a logistic regression to a sample of 141 small Italian enterprises located in the Marche region, and the authors build two different default prediction models: one using only financial ratios and one using jointly financial ratios and variables related to the relationship between firm and territory. Findings – Including variables regarding the relationships between firms and their territory, the accuracy rates of the default prediction model are significantly improved. Research limitations/implications – The qualitative variables data collected are affected by subjective judgments of respondents of the firms studied. In addition, neither other qualitative variables (such as those regarding competitive strategies, or managerial skills) are included nor those variables regarding the relationships between firms and financial institutions are included. Practical implications – The study suggests that financial institutions should include territory qualitative variables, and, above all, qualitative variables regarding the firm–territory relationship, when constructing business default prediction models. Including this type of variables, it could be able to reduce the tendency to place unnecessary restrictions on credit. Originality/value – The field of business failure prediction modeling using variables regarding the relationship between firm–territory is a unexplored area as it count of a very few studies.

A territorial perspective of SME’s default prediction models / Gabbianelli, Linda. - In: STUDIES IN ECONOMICS AND FINANCE. - ISSN 1086-7376. - 35:4(2018), pp. 542-563. [10.1108/SEF-08-2016-0207]

A territorial perspective of SME’s default prediction models

Gabbianelli, Linda
2018

Abstract

Purpose – The purpose of this paper is to test whether the qualitative variables regarding the territory and the firm–territory relationship can improve the accuracy rates of small business default prediction models. Design/methodology/approach – The authors apply a logistic regression to a sample of 141 small Italian enterprises located in the Marche region, and the authors build two different default prediction models: one using only financial ratios and one using jointly financial ratios and variables related to the relationship between firm and territory. Findings – Including variables regarding the relationships between firms and their territory, the accuracy rates of the default prediction model are significantly improved. Research limitations/implications – The qualitative variables data collected are affected by subjective judgments of respondents of the firms studied. In addition, neither other qualitative variables (such as those regarding competitive strategies, or managerial skills) are included nor those variables regarding the relationships between firms and financial institutions are included. Practical implications – The study suggests that financial institutions should include territory qualitative variables, and, above all, qualitative variables regarding the firm–territory relationship, when constructing business default prediction models. Including this type of variables, it could be able to reduce the tendency to place unnecessary restrictions on credit. Originality/value – The field of business failure prediction modeling using variables regarding the relationship between firm–territory is a unexplored area as it count of a very few studies.
2018
35
4
542
563
A territorial perspective of SME’s default prediction models / Gabbianelli, Linda. - In: STUDIES IN ECONOMICS AND FINANCE. - ISSN 1086-7376. - 35:4(2018), pp. 542-563. [10.1108/SEF-08-2016-0207]
Gabbianelli, Linda
File in questo prodotto:
File Dimensione Formato  
6Gabbianelli _ A territorial perspective of SME’s default prediction models..pdf

Accesso riservato

Tipologia: Versione pubblicata dall'editore
Dimensione 208.19 kB
Formato Adobe PDF
208.19 kB Adobe PDF   Visualizza/Apri   Richiedi una copia
Pubblicazioni consigliate

Licenza Creative Commons
I metadati presenti in IRIS UNIMORE sono rilasciati con licenza Creative Commons CC0 1.0 Universal, mentre i file delle pubblicazioni sono rilasciati con licenza Attribuzione 4.0 Internazionale (CC BY 4.0), salvo diversa indicazione.
In caso di violazione di copyright, contattare Supporto Iris

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1330546
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 8
  • ???jsp.display-item.citation.isi??? 7
social impact